In general, when searching for Web pages, typical Web search engines have the advantage of a database of textual information over which to search. Computers have been able to process textual information for decades. To find a Web page containing a specific word, a user merely needs to enter that word into a search engine search box and the search engine will attempt to find pages that contain the particular word.
When it comes to searching for images or other multimedia data, existing search algorithms and their implementations generally rely on descriptive cataloging. An image has, at a minimum, a file name, and optionally text tags, otherwise referred to as metadata, which may be interpreted as a title, description, or keywords. A database of cataloged images can have additional information included as well as real cataloging. For example, the records could list the artist, the date of production, the style, the theme, the colors, the reproduction technique, and so on. Yet cataloging the information content within images is a daunting task and, in most cases, goes incomplete or insufficient because visual content has a huge amount of useful data that may require tagging for searches, which is not practical to achieve.
The sound files, like images, may be indexed by their titles. Unfortunately, if it is simply an embedded or linked audio file on a Web page, there may be no additional information about it. The audio files may have some descriptive information included, such as the source. Other metadata can be included in audio files, but that requires more effort on the part of the content producer, and as in the case of images discussed above, this may be incomplete or insufficient, to say the least.
To fully index the content of audio files generally requires having a transcript of the session in a computer-readable text format to enable text-indexing. With voice recognition software, some automated indexing of audio files is possible and has been successfully used. However, it is widely known that such transcripts rarely match what was spoken exactly. The difficulty is compounded if the spoken words are sung and the search is for the song in a specific tune, or a search for a tune regardless of the words.
When combining the difficulties of searching for images with the difficulties of searching for audio files, one may begin to appreciate the exacerbated problems of searching for multimedia content such as video. Like audio, video comes in a variety of formats, filed or streamed, including AVI, MPEG, QuickTime®, Windows® Media, and Real®.
Video content is even more difficult to index because of the large amount of data that may require indexing, which includes images, or portions thereof, and text embedded in the image, as well as sounds. The amount of all kinds of multimedia content distributed via the Internet is growing rapidly, much of it being sparsely indexed, if at all. While general Web spiders can index file names of multimedia content, and possibly the anchor text that links to the files, they are unable to effectively index the actual content of the multimedia files and hence it is inaccessible to the search engine unless a transcript is available as well.
In view of the limitations of the prior art, it would be advantageous to provide a solution that enables automatic personalization of multimedia search. It would be further advantageous to enable the mapping of multimedia content to topics.